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Conference Paper

Transductive Inference for Estimating Values of Functions

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Chapelle, O., Vapnik, V., & Weston, J. (2000). Transductive Inference for Estimating Values of Functions. In S. Solla, T. Leen, & K. Müller (Eds.), Advances in Neural Information Processing Systems 12 (pp. 421-427). Cambridge, MA, USA: MIT Press.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E4CE-8
We introduce an algorithm for estimating the values of a function at a set of test points x_1^*,dots,x^*_m given a set of training points (x_1,y_1),dots,(x_ell,y_ell) without estimating (as an intermediate step) the regression function. We demonstrate that this direct (transductive) way for estimating values of the regression (or classification in pattern recognition) is more accurate than the traditional one based on two steps, first estimating the function and then calculating the values of this function at the points of interest.